Start refactor

Docstrings

Change "time slices" to "time points"

Working

Fix docs

Fix tests

Update changelog

Minor

CHANGELOG.md

@@ -20,6 +20,13 @@
   `action="count"`, so `-v` turns verbosity to INFO level,
   whereas `-vv` turns verbosity to DEBUG level.
 
+- The `return_posteriors=True` option with `method="inside_outside"`
+  previously returned a dict that included keys `start_time` and `end_time`,
+  giving the impression that the posterior for node age is discretized over
+  time slices in this algorithm. In actuality, the posterior is discretized
+  atomically over time points, so `start_time` and `end_time` have been
+  replaced by a single key `time`.
+
 - Python 3.7 is no longer supported.
 
 **Features**

docs/priors.md

@@ -54,7 +54,7 @@ See below for more explanation of the interpretation of the parameters passed to
 For {ref}`sec_methods_discrete_time` methods, it is possible to switch from the
 (default) lognormal approximation to a gamma distribution, used when building a
 mixture prior for nodes that have variable numbers of children in different
-genomic regions. The discretized prior is then constructed by evaluating the
+genomic regions. The discretised prior is then constructed by evaluating the
 lognormal (or gamma) distribution across a grid of fixed times. Tests have shown that the
 lognormal is usually a better fit to the true prior in most cases.
 

docs/python-api.md

@@ -25,6 +25,8 @@ This page provides formal documentation for the `tsdate` Python API.
 
 ```{eval-rst}
 .. autofunction:: tsdate.date
+.. autofunction:: tsdate.discretised_dates
+.. autofunction:: tsdate.variational_dates
 ```
 
 ## Prior and Time Discretisation Options

docs/usage.md

@@ -255,7 +255,7 @@ different stages of dating will take.
 
 If the {ref}`method<sec_methods>` used for dating involves discrete time slices, `tsdate` scales
 quadratically in the number of time slices chosen. For greater temporal resolution,
-you are thus advised to use the `variational_gamma` method, which does not discretize time.
+you are thus advised to use the `variational_gamma` method, which does not discretise time.
 
 #### Optimisations
 

tests/test_functions.py

@@ -41,11 +41,11 @@
 from tsdate import base
 from tsdate.core import constrain_ages_topo
 from tsdate.core import date
-from tsdate.core import get_dates
+from tsdate.core import discretised_dates
+from tsdate.core import discretised_mean_var
 from tsdate.core import InOutAlgorithms
 from tsdate.core import Likelihoods
 from tsdate.core import LogLikelihoods
-from tsdate.core import posterior_mean_var
 from tsdate.core import variational_dates
 from tsdate.core import VariationalLikelihoods
 from tsdate.demography import PopulationSizeHistory
@@ -500,7 +500,7 @@ def test_custom_timegrid_is_not_rescaled(self):
         prior = MixturePrior(ts)
         demography = PopulationSizeHistory(3)
         timepoints = np.array([0, 300, 1000, 2000])
-        prior_grid = prior.make_discretized_prior(demography, timepoints=timepoints)
+        prior_grid = prior.make_discretised_prior(demography, timepoints=timepoints)
         assert np.array_equal(prior_grid.timepoints, timepoints)
 
 
@@ -1602,35 +1602,16 @@ def test_bad_Ne(self):
             tsdate.build_prior_grid(ts, population_size=-10)
 
 
-class TestCallingErrors:
-    def test_bad_vgamma_probability_space(self):
-        ts = utility_functions.single_tree_ts_n2()
-        with pytest.raises(ValueError, match="Cannot specify"):
-            variational_dates(ts, 1, 1, probability_space=base.LOG)
-
-    def test_bad_vgamma_num_threads(self):
-        # Test can be removed if we specify num_threads in the future
-        ts = utility_functions.single_tree_ts_n2()
-        with pytest.raises(ValueError, match="does not currently"):
-            variational_dates(ts, 1, 1, num_threads=2)
-
-    def test_bad_vgamma_ignore_oldest_root(self):
-        # Test can be removed in the future if this is implemented
-        ts = utility_functions.single_tree_ts_n2()
-        with pytest.raises(ValueError, match="not implemented"):
-            variational_dates(ts, 1, 1, ignore_oldest_root=True)
-
-
-class TestPosteriorMeanVar:
+class TestDiscretisedMeanVar:
     """
-    Test posterior_mean_var works as expected
+    Test discretised_mean_var works as expected
     """
 
-    def test_posterior_mean_var(self):
+    def test_discretised_mean_var(self):
         ts = utility_functions.single_tree_ts_n2()
         for distr in ("gamma", "lognorm"):
             posterior, algo = TestTotalFunctionalValueTree().find_posterior(ts, distr)
-            ts_node_metadata, mn_post, vr_post = posterior_mean_var(ts, posterior)
+            mn_post, vr_post = discretised_mean_var(ts, posterior)
             assert np.array_equal(
                 mn_post,
                 [
@@ -1640,19 +1621,12 @@ def test_posterior_mean_var(self):
                 ],
             )
 
-    def test_node_metadata_single_tree_n2(self):
-        ts = utility_functions.single_tree_ts_n2()
-        posterior, algo = TestTotalFunctionalValueTree().find_posterior(ts, "lognorm")
-        ts_node_metadata, mn_post, vr_post = posterior_mean_var(ts, posterior)
-        assert json.loads(ts_node_metadata.node(2).metadata)["mn"] == mn_post[2]
-        assert json.loads(ts_node_metadata.node(2).metadata)["vr"] == vr_post[2]
-
     def test_node_metadata_simulated_tree(self):
         larger_ts = msprime.simulate(
             10, mutation_rate=1, recombination_rate=1, length=20, random_seed=12
         )
-        _, mn_post, _, _, eps, _, _ = get_dates(
-            larger_ts, mutation_rate=None, population_size=10000
+        mn_post, *_ = discretised_dates(
+            larger_ts, mutation_rate=None, population_size=10000, eps=1e-6
         )
         dated_ts = date(larger_ts, population_size=10000, mutation_rate=None)
         metadata = dated_ts.tables.nodes.metadata
@@ -1866,8 +1840,8 @@ def test_node_selection_param(self):
     def test_sites_time_insideoutside(self):
         ts = utility_functions.two_tree_mutation_ts()
         dated = tsdate.date(ts, mutation_rate=None, population_size=1)
-        _, mn_post, _, _, eps, _, _ = get_dates(
-            ts, mutation_rate=None, population_size=1
+        mn_post, *_ = discretised_dates(
+            ts, mutation_rate=None, population_size=1, eps=1e-6
         )
         assert np.array_equal(
             mn_post[ts.tables.mutations.node],
@@ -1971,7 +1945,7 @@ def test_sites_time_simulated(self):
         larger_ts = msprime.simulate(
             10, mutation_rate=1, recombination_rate=1, length=20, random_seed=12
         )
-        _, mn_post, _, _, _, _, _ = get_dates(
+        mn_post, *_ = discretised_dates(
             larger_ts, mutation_rate=None, population_size=10000
         )
         dated = date(larger_ts, mutation_rate=None, population_size=10000)

tests/test_inference.py

@@ -128,27 +128,37 @@ def test_no_posteriors(self):
             method="maximization",
             mutation_rate=1,
         )
-        assert len(posteriors) == ts.num_nodes - ts.num_samples + 2
-        assert len(posteriors["start_time"]) == len(posteriors["end_time"])
-        assert len(posteriors["start_time"]) > 0
+        assert posteriors is None
+
+    def test_discretised_posteriors(self):
+        ts = utility_functions.two_tree_mutation_ts()
+        ts, posteriors = tsdate.date(
+            ts, mutation_rate=None, population_size=1, return_posteriors=True
+        )
+        assert len(posteriors) == ts.num_nodes - ts.num_samples + 1
+        assert len(posteriors["time"]) > 0
         for node in ts.nodes():
             if not node.is_sample():
                 assert node.id in posteriors
-                assert posteriors[node.id] is None
+                assert len(posteriors[node.id]) == len(posteriors["time"])
+                assert np.isclose(np.sum(posteriors[node.id]), 1)
 
-    def test_posteriors(self):
+    def test_variational_posteriors(self):
         ts = utility_functions.two_tree_mutation_ts()
         ts, posteriors = tsdate.date(
-            ts, mutation_rate=None, population_size=1, return_posteriors=True
+            ts,
+            mutation_rate=1e-2,
+            population_size=1,
+            method="variational_gamma",
+            return_posteriors=True,
         )
-        assert len(posteriors) == ts.num_nodes - ts.num_samples + 2
-        assert len(posteriors["start_time"]) == len(posteriors["end_time"])
-        assert len(posteriors["start_time"]) > 0
+        assert len(posteriors) == ts.num_nodes - ts.num_samples + 1
+        assert len(posteriors["parameter"]) == 2
         for node in ts.nodes():
             if not node.is_sample():
                 assert node.id in posteriors
-                assert len(posteriors[node.id]) == len(posteriors["start_time"])
-                assert np.isclose(np.sum(posteriors[node.id]), 1)
+                assert len(posteriors[node.id]) == 2
+                assert np.all(posteriors[node.id] > 0)
 
     def test_marginal_likelihood(self):
         ts = utility_functions.two_tree_mutation_ts()
@@ -419,7 +429,7 @@ def test_nonglobal_priors(self):
 
     def test_bad_arguments(self):
         ts = utility_functions.two_tree_mutation_ts()
-        with pytest.raises(ValueError, match="Maximum number of iterations"):
+        with pytest.raises(ValueError, match="Maximum number of EP iterations"):
             tsdate.date(
                 ts,
                 mutation_rate=5,
@@ -435,13 +445,13 @@ def test_match_central_moments(self):
             mutation_rate=5,
             population_size=1,
             method="variational_gamma",
-            method_of_moments=False,
+            match_central_moments=False,
         )
         ts1 = tsdate.date(
             ts,
             mutation_rate=5,
             population_size=1,
             method="variational_gamma",
-            method_of_moments=True,
+            match_central_moments=True,
         )
         assert np.any(np.not_equal(ts0.nodes_time, ts1.nodes_time))

tsdate/__init__.py

@@ -21,7 +21,8 @@
 # SOFTWARE.
 from .cache import *  # NOQA: F401,F403
 from .core import date  # NOQA: F401
-from .core import get_dates  # NOQA: F401
+from .core import discretised_dates  # NOQA: F401
+from .core import variational_dates  # NOQA: F401
 from .prior import build_grid as build_prior_grid  # NOQA: F401
 from .prior import parameter_grid as build_parameter_grid  # NOQA: F401
 from .provenance import __version__  # NOQA: F401